Schapira Matthieu, Raaka Bruce M, Das Sharmistha, Fan Li, Totrov Maxim, Zhou Zhiguo, Wilson Stephen R, Abagyan Ruben, Samuels Herbert H
Molsoft LLC, 3366 North Torrey Pines Court, Suite 300, La Jolla, CA 92037, USA.
Proc Natl Acad Sci U S A. 2003 Jun 10;100(12):7354-9. doi: 10.1073/pnas.1131854100. Epub 2003 May 30.
Treatment of hyperthyroidism, a common clinical condition that can have serious manifestations in the elderly, has remained essentially unchanged for >30 years. Directly antagonizing the effect of the thyroid hormone at the receptor level may be a significant improvement for the treatment of hyperthyroid patients. We built a computer model of the thyroid hormone receptor (TR) ligand-binding domain in its predicted antagonist-bound conformation and used a virtual screening algorithm to select 100 TR antagonist candidates out of a library of >250,000 compounds. We were able to obtain 75 of the compounds selected in silico and studied their ability to act as antagonists by using cultured cells that express TR. Fourteen of these compounds were found to antagonize the effect of T3 on TR with IC50s ranging from 1.5 to 30 microM. A small virtual library of compounds, derived from the highest affinity antagonist (1-850) that could be rapidly synthesized, was generated. A second round of virtual screening identified new compounds with predicted increased antagonist activity. These second generation compounds were synthesized, and their ability to act as TR antagonists was confirmed by transfection and receptor binding experiments. The extreme structural diversity of the antagonist compounds shows how receptor-based virtual screening can identify diverse chemistries that comply with the structural rules of TR antagonism.
甲状腺功能亢进症是一种常见的临床病症,在老年人中可能会有严重表现,其治疗方法在30多年来基本没有变化。在受体水平直接拮抗甲状腺激素的作用可能是治疗甲状腺功能亢进症患者的一项重大进展。我们构建了处于预测的拮抗剂结合构象的甲状腺激素受体(TR)配体结合域的计算机模型,并使用虚拟筛选算法从超过250,000种化合物的库中选择了100种TR拮抗剂候选物。我们能够获得在计算机模拟中选择的75种化合物,并通过使用表达TR的培养细胞研究它们作为拮抗剂的能力。发现其中14种化合物可拮抗T3对TR的作用,IC50范围为1.5至30 microM。从可快速合成的最高亲和力拮抗剂(1-850)衍生出一个小型化合物虚拟库。第二轮虚拟筛选鉴定出预测具有增强拮抗剂活性的新化合物。合成了这些第二代化合物,并通过转染和受体结合实验证实了它们作为TR拮抗剂的能力。拮抗剂化合物的极端结构多样性表明基于受体的虚拟筛选如何能够识别符合TR拮抗结构规则的多种化学物质。